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• Guide to Experimental Design | Overview, Steps, & Examples

Guide to Experimental Design | Overview, 5 steps & Examples

Published on December 3, 2019 by Rebecca Bevans . Revised on June 21, 2023.

Experiments are used to study causal relationships . You manipulate one or more independent variables and measure their effect on one or more dependent variables.

Experimental design create a set of procedures to systematically test a hypothesis . A good experimental design requires a strong understanding of the system you are studying.

There are five key steps in designing an experiment:

• Consider your variables and how they are related
• Write a specific, testable hypothesis
• Design experimental treatments to manipulate your independent variable
• Assign subjects to groups, either between-subjects or within-subjects
• Plan how you will measure your dependent variable

For valid conclusions, you also need to select a representative sample and control any  extraneous variables that might influence your results. If random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead. This minimizes several types of research bias, particularly sampling bias , survivorship bias , and attrition bias as time passes.

Table of contents

Step 1: define your variables, step 2: write your hypothesis, step 3: design your experimental treatments, step 4: assign your subjects to treatment groups, step 5: measure your dependent variable, other interesting articles, frequently asked questions about experiments.

You should begin with a specific research question . We will work with two research question examples, one from health sciences and one from ecology:

To translate your research question into an experimental hypothesis, you need to define the main variables and make predictions about how they are related.

Start by simply listing the independent and dependent variables .

Then you need to think about possible extraneous and confounding variables and consider how you might control  them in your experiment.

Finally, you can put these variables together into a diagram. Use arrows to show the possible relationships between variables and include signs to show the expected direction of the relationships.

Here we predict that increasing temperature will increase soil respiration and decrease soil moisture, while decreasing soil moisture will lead to decreased soil respiration.

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Now that you have a strong conceptual understanding of the system you are studying, you should be able to write a specific, testable hypothesis that addresses your research question.

The next steps will describe how to design a controlled experiment . In a controlled experiment, you must be able to:

• Systematically and precisely manipulate the independent variable(s).
• Precisely measure the dependent variable(s).
• Control any potential confounding variables.

If your study system doesn’t match these criteria, there are other types of research you can use to answer your research question.

How you manipulate the independent variable can affect the experiment’s external validity – that is, the extent to which the results can be generalized and applied to the broader world.

First, you may need to decide how widely to vary your independent variable.

• just slightly above the natural range for your study region.
• over a wider range of temperatures to mimic future warming.
• over an extreme range that is beyond any possible natural variation.

Second, you may need to choose how finely to vary your independent variable. Sometimes this choice is made for you by your experimental system, but often you will need to decide, and this will affect how much you can infer from your results.

• a categorical variable : either as binary (yes/no) or as levels of a factor (no phone use, low phone use, high phone use).
• a continuous variable (minutes of phone use measured every night).

How you apply your experimental treatments to your test subjects is crucial for obtaining valid and reliable results.

First, you need to consider the study size : how many individuals will be included in the experiment? In general, the more subjects you include, the greater your experiment’s statistical power , which determines how much confidence you can have in your results.

Then you need to randomly assign your subjects to treatment groups . Each group receives a different level of the treatment (e.g. no phone use, low phone use, high phone use).

You should also include a control group , which receives no treatment. The control group tells us what would have happened to your test subjects without any experimental intervention.

When assigning your subjects to groups, there are two main choices you need to make:

• A completely randomized design vs a randomized block design .
• A between-subjects design vs a within-subjects design .

Randomization

An experiment can be completely randomized or randomized within blocks (aka strata):

• In a completely randomized design , every subject is assigned to a treatment group at random.
• In a randomized block design (aka stratified random design), subjects are first grouped according to a characteristic they share, and then randomly assigned to treatments within those groups.

Sometimes randomization isn’t practical or ethical , so researchers create partially-random or even non-random designs. An experimental design where treatments aren’t randomly assigned is called a quasi-experimental design .

Between-subjects vs. within-subjects

In a between-subjects design (also known as an independent measures design or classic ANOVA design), individuals receive only one of the possible levels of an experimental treatment.

In medical or social research, you might also use matched pairs within your between-subjects design to make sure that each treatment group contains the same variety of test subjects in the same proportions.

In a within-subjects design (also known as a repeated measures design), every individual receives each of the experimental treatments consecutively, and their responses to each treatment are measured.

Within-subjects or repeated measures can also refer to an experimental design where an effect emerges over time, and individual responses are measured over time in order to measure this effect as it emerges.

Counterbalancing (randomizing or reversing the order of treatments among subjects) is often used in within-subjects designs to ensure that the order of treatment application doesn’t influence the results of the experiment.

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Finally, you need to decide how you’ll collect data on your dependent variable outcomes. You should aim for reliable and valid measurements that minimize research bias or error.

Some variables, like temperature, can be objectively measured with scientific instruments. Others may need to be operationalized to turn them into measurable observations.

• Ask participants to record what time they go to sleep and get up each day.
• Ask participants to wear a sleep tracker.

How precisely you measure your dependent variable also affects the kinds of statistical analysis you can use on your data.

Experiments are always context-dependent, and a good experimental design will take into account all of the unique considerations of your study system to produce information that is both valid and relevant to your research question.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

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Experimental Research Design — 6 mistakes you should never make!

Since school days’ students perform scientific experiments that provide results that define and prove the laws and theorems in science. These experiments are laid on a strong foundation of experimental research designs.

An experimental research design helps researchers execute their research objectives with more clarity and transparency.

In this article, we will not only discuss the key aspects of experimental research designs but also the issues to avoid and problems to resolve while designing your research study.

Table of Contents

What Is Experimental Research Design?

Experimental research design is a framework of protocols and procedures created to conduct experimental research with a scientific approach using two sets of variables. Herein, the first set of variables acts as a constant, used to measure the differences of the second set. The best example of experimental research methods is quantitative research .

Experimental research helps a researcher gather the necessary data for making better research decisions and determining the facts of a research study.

When Can a Researcher Conduct Experimental Research?

A researcher can conduct experimental research in the following situations —

• When time is an important factor in establishing a relationship between the cause and effect.
• When there is an invariable or never-changing behavior between the cause and effect.
• Finally, when the researcher wishes to understand the importance of the cause and effect.

Importance of Experimental Research Design

To publish significant results, choosing a quality research design forms the foundation to build the research study. Moreover, effective research design helps establish quality decision-making procedures, structures the research to lead to easier data analysis, and addresses the main research question. Therefore, it is essential to cater undivided attention and time to create an experimental research design before beginning the practical experiment.

By creating a research design, a researcher is also giving oneself time to organize the research, set up relevant boundaries for the study, and increase the reliability of the results. Through all these efforts, one could also avoid inconclusive results. If any part of the research design is flawed, it will reflect on the quality of the results derived.

Types of Experimental Research Designs

Based on the methods used to collect data in experimental studies, the experimental research designs are of three primary types:

1. Pre-experimental Research Design

A research study could conduct pre-experimental research design when a group or many groups are under observation after implementing factors of cause and effect of the research. The pre-experimental design will help researchers understand whether further investigation is necessary for the groups under observation.

Pre-experimental research is of three types —

• One-shot Case Study Research Design
• One-group Pretest-posttest Research Design
• Static-group Comparison

2. True Experimental Research Design

A true experimental research design relies on statistical analysis to prove or disprove a researcher’s hypothesis. It is one of the most accurate forms of research because it provides specific scientific evidence. Furthermore, out of all the types of experimental designs, only a true experimental design can establish a cause-effect relationship within a group. However, in a true experiment, a researcher must satisfy these three factors —

• There is a control group that is not subjected to changes and an experimental group that will experience the changed variables
• A variable that can be manipulated by the researcher
• Random distribution of the variables

This type of experimental research is commonly observed in the physical sciences.

3. Quasi-experimental Research Design

The word “Quasi” means similarity. A quasi-experimental design is similar to a true experimental design. However, the difference between the two is the assignment of the control group. In this research design, an independent variable is manipulated, but the participants of a group are not randomly assigned. This type of research design is used in field settings where random assignment is either irrelevant or not required.

The classification of the research subjects, conditions, or groups determines the type of research design to be used.

Advantages of Experimental Research

Experimental research allows you to test your idea in a controlled environment before taking the research to clinical trials. Moreover, it provides the best method to test your theory because of the following advantages:

• Researchers have firm control over variables to obtain results.
• The subject does not impact the effectiveness of experimental research. Anyone can implement it for research purposes.
• The results are specific.
• Post results analysis, research findings from the same dataset can be repurposed for similar research ideas.
• Researchers can identify the cause and effect of the hypothesis and further analyze this relationship to determine in-depth ideas.
• Experimental research makes an ideal starting point. The collected data could be used as a foundation to build new research ideas for further studies.

6 Mistakes to Avoid While Designing Your Research

There is no order to this list, and any one of these issues can seriously compromise the quality of your research. You could refer to the list as a checklist of what to avoid while designing your research.

1. Invalid Theoretical Framework

Usually, researchers miss out on checking if their hypothesis is logical to be tested. If your research design does not have basic assumptions or postulates, then it is fundamentally flawed and you need to rework on your research framework.

2. Inadequate Literature Study

Without a comprehensive research literature review , it is difficult to identify and fill the knowledge and information gaps. Furthermore, you need to clearly state how your research will contribute to the research field, either by adding value to the pertinent literature or challenging previous findings and assumptions.

3. Insufficient or Incorrect Statistical Analysis

Statistical results are one of the most trusted scientific evidence. The ultimate goal of a research experiment is to gain valid and sustainable evidence. Therefore, incorrect statistical analysis could affect the quality of any quantitative research.

4. Undefined Research Problem

This is one of the most basic aspects of research design. The research problem statement must be clear and to do that, you must set the framework for the development of research questions that address the core problems.

5. Research Limitations

Every study has some type of limitations . You should anticipate and incorporate those limitations into your conclusion, as well as the basic research design. Include a statement in your manuscript about any perceived limitations, and how you considered them while designing your experiment and drawing the conclusion.

6. Ethical Implications

The most important yet less talked about topic is the ethical issue. Your research design must include ways to minimize any risk for your participants and also address the research problem or question at hand. If you cannot manage the ethical norms along with your research study, your research objectives and validity could be questioned.

Experimental Research Design Example

In an experimental design, a researcher gathers plant samples and then randomly assigns half the samples to photosynthesize in sunlight and the other half to be kept in a dark box without sunlight, while controlling all the other variables (nutrients, water, soil, etc.)

By comparing their outcomes in biochemical tests, the researcher can confirm that the changes in the plants were due to the sunlight and not the other variables.

Experimental research is often the final form of a study conducted in the research process which is considered to provide conclusive and specific results. But it is not meant for every research. It involves a lot of resources, time, and money and is not easy to conduct, unless a foundation of research is built. Yet it is widely used in research institutes and commercial industries, for its most conclusive results in the scientific approach.

Have you worked on research designs? How was your experience creating an experimental design? What difficulties did you face? Do write to us or comment below and share your insights on experimental research designs!

Frequently Asked Questions

Randomization is important in an experimental research because it ensures unbiased results of the experiment. It also measures the cause-effect relationship on a particular group of interest.

Experimental research design lay the foundation of a research and structures the research to establish quality decision making process.

There are 3 types of experimental research designs. These are pre-experimental research design, true experimental research design, and quasi experimental research design.

The difference between an experimental and a quasi-experimental design are: 1. The assignment of the control group in quasi experimental research is non-random, unlike true experimental design, which is randomly assigned. 2. Experimental research group always has a control group; on the other hand, it may not be always present in quasi experimental research.

Experimental research establishes a cause-effect relationship by testing a theory or hypothesis using experimental groups or control variables. In contrast, descriptive research describes a study or a topic by defining the variables under it and answering the questions related to the same.

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Experimental Research Designs

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Experimental Research Designs

Oct 29, 2019

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Experimental Research Designs. Dr Leena KC Dean ,Yenepoya Nursing college, Mangalore. Experimental Research. Researcher’s role vis-à-vis study participants In some studies researcher wants to test the effects of a specific intervention Such studies are experimental studies

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Presentation Transcript

Experimental Research Designs Dr Leena KC Dean ,Yenepoya Nursing college, Mangalore

Experimental Research • Researcher’s role vis-à-vis study participants • In some studies researcher wants to test the effects of a specific intervention • Such studies are experimental studies • The researcher play an active role by introducing the intervention • Researchers are active agents & not passive observers

Characteristics of true experiments • Manipulation: experimenter does something to at least some subjects • Control: experimenter introduces controls over the experimental situation including use of a control group • Randomization: experimenter assigns subjects to a control or experimental group on a random basis

Manipulation • Involves doing something to study participants (Independent variable) • Experimental treatment • Intervention • Vary the IV and observe effect on the DV • Cause and effect

Control • Achieved by • Manipulating • Randomizing • Carefully preparing experimental protocols • Using a control group

Control group • Group of subjects whose performance on a DV is used to evaluate the performance of the experimental group (treatment group) on the same DV

Randomization (random assignment) • Placing subjects in groups at random. • Random: every subject has an equal chance of being assigned to any group • No systematic bias in the group with respect to attributes that could affect the DV

Methods of randomization • Flipping a coin • Matching • Draw of lots (pulling names from a hat) • Table of random numbers • Cluster randomization: reduces possibility of contamination • Computer generated random numbers: • Randomization.com

Randomization.com!!! (where it's never the same thing twice) There are three randomization plan generators. • The first (and original) generator randomizes each subject to a single treatment by using the method of randomly permuted blocks. • The second generator creates random permutations of treatments for situations where subjects are to receive all of the treatments in random order. • The third generator generates a random permutation of integers. This is particularly useful for selecting a sample without replacement.

Control over independent variable • Experimental • Quasi experimental • Pre-experimental • Non experimental

Is there an intervention? (control over the IV) No yes Non experimental research Is there random assignment to treatment groups? Yes Experimental research

Is there an intervention? (control over the IV) yes No Non experimental research Is there random assignment to treatment groups? No Yes Are there efforts to compensate for the lack of random assignment Experimental research Yes No Pre-experimental research Quasi experimental research

Experimental designs

Basic experimental designs • Pretest- posttest design (before-after design) • Posttest only design (after-only design) • Solomon four- group design • Factorial design • Randomized block design • Crossover design (repeated measure design)

Pretest- posttest design (before-after design) • Classic experimental design • Most commonly used • Two randomized groups • One group receives the experimental treatment • Other group receiving no treatment, a placebo treatment, or the usual treatment

Pretest- posttest design (before-after design) • Comparison of the pretest scores allows evaluation of the effectiveness of randomization • Treatment is under the control of the researcher • The dependent variable is measured twice, before & after the manipulation of the independent variable • Comparison of the posttest scores allows determining the effect of the independent variable

Pretest- posttest design (before-after design) Group Pretest Intervention Posttest R C O1 O2 R E O1 X O2

Pretest- posttest design (before-after design) • Impact of an adaptation training program for patients with end stage renal disease. Shiow, Yuh & Ching Lee (2005) • 57 patients randomly assigned to two groups • Intervention:Adaptation training program: group sessions over 8 week period & monthly follow up • Controlgroup received usual care • Outcome measure: QOL, HD stressor scale, depression inventory, medical outcomes at 0 & 3 months

Pretest- posttest design (before-after design) • Effectiveness of accupressure in improving dyspnea in COPD patients • 44 patients randomly assigned to two groups • Intervention:true accupoint pressure, 5 sessions per week & 16 minutes per session. 20 sessions over 4 weeks period • Controlgroup: accupressure using sham pressure points for the same duration • Outcome measure: pulmonary function status, dyspnea questionnaire, anxiety & 6 minute walk test, at 0 & 4 wks

Posttest only design (after-only design) Group Pretest Intervention Posttest R C O R E X O

Posttest only design (after-only design) • Effect of two nursing interventions in preventing occurrence of angular stomatitis on orally intubated patients. Rani Jose (2005) • 40 patients randomized to two treatment groups (odd & even numbers) • E1 Angle changing 12 hrly • E2 Hydrocolloid application at the angle without changing the position • E1 Daily assessment of angle & lips • E2 Assessment of angle & lips on day 3

Posttest only design (after-only design) Group Intervention Posttest R E1 X1 O R E2 X2 O

Posttest only design (after-only design) • Effect of a new educational strategy on students’ attitudes towards patients • Reduces the effect of pretest sensitization

Solomon four group design • Involves two experimental groups & two control groups • One experimental & one control group are administered pretest & other groups are not • Allows the effects of pretest measure & intervention to be segregated

Solomon four- group design Group Pretest Intervention Posttest C O1 O2 C O2 E O1 X O2 E X O2

Factorial design • Used to manipulate two or more independent variables simultaneously • Independent Variables - Factors • Each factor – levels (two or more) • Treatment condition – cell (Box) • Designs - 2 x 2, 2 x 3, 3 x 3, 3 x 4

Factorial design (2X3)

Factorial design (2X3)Intervention for pain relief in cancer patients

Randomized block design • Similar to factorial design • Two Factors • One factor not experimentally manipulated -Blocking variable • Designs- 2 X 2, 2 X 3, 2 X 2 X 2

Randomized block design (2x2)

Crossover design (repeated measure or counterbalanced design) • Involves exposure of same subjects to more than one experimental treatment • Subjects are randomly assigned to different orderings of treatment • Subjects serve as their own control • Extremely powerful design but inappropriate for certain types of research due to problem of carry-over effects

Crossover design • Effects of soy protein rich diet on renal function in young adults with insulin dependent DM. Stephenson et al (2005) • 12 Type I DM patients • 4 weeks assessment of baseline data • Assigned to soy or control diet for 8 weeks • Crossed over to alternative diet for 8 wks • Outcome measures: GFR, LDL cholesterol, urinary excretion of soy isofavones at the end of each 8 wk period

Experimental condition(formal protocol) • What is the intervention? How does it differ from usual methods of care? • If there are two alternative interventions, how exactly do they differ? • What are the specific procedures to be used with those receiving the intervention? • What is the dosage or intensity of the intervention

Experimental condition • Over how long a period will the intervention be administered, how frequently it will be administered, and when will the treatment begin? • Who will administer the intervention? What are their credentials, and what type of special training will they receive? • Under what conditions will the intervention be withdrawn or altered?

Control condition • Control group condition used as a basis of comparison in a study is referred to as the counterfactual • An alternative intervention • A placebo or psuedointervention presumed to have no therapeutic value • Standard methods of care • Different doses or intensities of treatment • Delayed treatment

Control condition • Needs to be as carefully spelt out as the intervention • Explain what the condition was & how different it was from the intervention being tested

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• Preferences

Experimental Research Designs - PowerPoint PPT Presentation

Experimental Research Designs

Note: bring measurement plan. in small groups. read each others measurement plans ... following the treatment, all participants are measured on the dependent variable ... – powerpoint ppt presentation.

• Note Bring measurement plan
• Read each others measurement plans
• How is/are the IV(s) measured?
• How is/are the DV(s) measured?
• How do the variables vary?
• Has the writer addressed reliability?
• Has the writer addressed validity?
• Can demonstrate cause-and-effect very convincingly
• Very stringent research design requirements
• Experimental design requires
• Random assignment to groups (experimental and control)
• Independent treatment variable that can be applied to the experimental group
• Dependent variable that can be measured in all groups
• Used in place of experimental research when random assignment to groups is not feasible
• Otherwise, very similar to true experimental research
• Explores the possibility of cause-and-effect relationships when experimental and quasi-experimental approaches are not feasible
• Used when manipulation of the independent variable is not ethical or is not possible
• External validityextent to which the results can be generalized to other groups or settings
• Population validitydegree of similarity among sample used, population from which it came, and target population
• Ecological validityphysical or emotional situation or setting that may have been unique to the experiment
• If the treatment effects can be obtained only under a limited set of conditions or only by the original researcher the findings have low ecological validity.
• Internal validityextent to which differences on the dependent variable are a direct result of the manipulation of the independent variable
• Historywhen factors other than treatment can exert influence over the results problematic over time
• Maturationwhen changes occur in dependent variable that may be due to natural developmental changes problematic over time
• Testingalso known as pretest sensitization pretest may give clues to treatment or posttest and may result in improved posttest scores
• Instrumentation Nature of outcome measure has changed.
• Regression Tendency of extreme scores to be nearer to the mean at retest
• Implementation-A group treated in an unintentional differential manner.
• Attitude-Hawthorne effect, compensatory rivalry.
• Differential selection of participantsparticipant s are not selected/assigned randomly
• Attrition (mortality)loss of participants
• Experimental treatment diffusion Control conditions receive experimental treatment.
• Commonly used experimental design notation
• X1 treatment group
• X2 control/comparison group
• O observation (pretest, posttest, etc.)
• R random assignment
• Single-group pretest-treatment-posttest design
• Technically, a pre-experimental design (only one group therefore, no random assignment exists)
• Overall, a weak design
• Two-group treatment-posttest-only design
• Here, we have random assignment to experimental, control groups
• A better design, but still weakcannot be sure that groups were equivalent to begin with
• Two-group pretest-treatment-posttest design
• A substantially improved designpreviously identified errors have been reduced
• Solomon four-group design
• A much improved designhow??
• One serious drawbackrequires twice as many participants
• Factorial designs
• Incorporates two or more factors
• Enables researcher to detect differential differences (effects apparent only on certain combinations of levels of independent variables)
• Single-participant measurement-treatment-measureme nt designs
• Purpose is to monitor effects on one subject
• Results can be generalized only with great caution
• Posttest-only design with nonequivalent groups
• Uses two groups from same population
• Questions must be addressed regarding equivalency of groups prior to introduction of treatment
• Pretest-posttest design with nonequivalent groups
• A stronger designpretest may be used to establish group equivalency
• Cause-and-effect relationship is hypothesized
• Participants are randomly assigned (experimental) or nonrandomly assigned (quasi-experimental)
• Application of an experimental treatment by researcher
• Following the treatment, all participants are measured on the dependent variable
• Data are usually quantitative and analyzed by looking for significant differences on the dependent variable
• Major recommendations for defining and operationalizing the instructional approach
• Avoid the nominal fallacy by carefully labeling and describing the independent variables
• Search for unanticipated effects that may be produced by the intervention
• Address assessment of implementation using standard checklists and in-depth methods
• Carefully document what happens in comparison classrooms
• Recommendations for probing the nature of the independent variable
• Provide a thorough description of samples
• Strive for random assignment
• Explore other alternative designs, such as formative or design experiments
• Quasi-experiments need to be critically reviewed
• Pretest variables should not show large differences (.5sd)
• Thorough sample description and analysis of comparison groups is essential.
• Recommendations regarding the use of dependent measures
• Select some measures that are not aligned tightly to the intervention
• Ensure that all measures are not experimenter developed and that some have been validated in prior research.
• Seek a balance between global and specific measures
• Look at intervention research as an opportunity to really build understanding of measures
• The importance of replication
• Researchers not interested in development of the independent variable should be involved
• What information does the public want from a School Report Card? (Adapted from Osowski)
• Does dual language instruction result in academic achievement?
• Chapter Eleven
• Refer to certain procedures that allow researchers to make inferences about a population based on data obtained from a sample.
• Obtaining a random sample is desirable since it ensures that this sample is representative of a larger population.
• The better a sample represents a population, the more researchers will be able to make inferences.
• Making inferences about populations is what Inferential Statistics are all about.
• It is reasonable to assume that each sample will give you a fairly accurate picture of its population.
• However, samples are not likely to be identical to their parent populations.
• This difference between a sample and its population is known as Sampling Error.
• Furthermore, no two samples will be identical in all their characteristics.
• There are times where large collections of random samples do pattern themselves in ways that will allow researchers to predict accurately some characteristics of the population from which the sample was taken.
• A sampling distribution of means is a frequency distribution resulting from plotting the means of a very large number of samples from the same population
• The standard deviation of a sampling distribution of means is called the Standard Error of the Mean (SEM).
• If you can accurately estimate the mean and the standard deviation of the sampling distribution, you can determine whether it is likely or not that a particular sample mean could be obtained from the population.
• To estimate the SEM, divide the SD of the sample by the square root of the sample size minus one.
• A Confidence Interval is a region extending both above and below a sample statistic within which a population parameter may be said to fall with a specified probability of being wrong.
• SEMs can be used to determine boundaries or limits, within which the population mean lies.
• If a confidence interval is 95, there would be a probability that 5 out of 100 (population mean) would fall outside the boundaries or limits.

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